Download JAR files tagged by machine with all dependencies

Azure Video Analyzer provides a platform to build intelligent video applications that span the edge and the cloud. The platform offers the capability to capture, record, and analyze live videos along with publishing the results, video, and video analytics, to Azure services in the cloud or the edge. It is designed to be an extensible platform, enabling you to connect different video analysis edge modules such as Cognitive services containers, custom edge modules built by you with open-source machine learning models or custom models trained with your own data. You can then use them to analyze live video without worrying about the complexity of building and running a live video pipeline. Use the client library for Video Analyzer Edge to simplify interactions with the Microsoft Azure IoT SDKs (https://github.com/azure/azure-iot-sdks) and programmatically construct pipeline topologies and live pipelines.

Rule Learner is an open source Java library for learning business rules from historical data. It supports supervised Machine Learning by incorporating well-known algorithms such as RIPPER and C4.5 provided by open source tools such as Weka. Rule Learner is implemented as a Machine Learning component of OpenRules Decision Manager. It allows business analysts to place their historical data in simple Excel tables and to automatically discover business rules that find certain pattern in this data. The generated rules are human readable and presented in Excel format executable by OpenRules Decision Manager.

The Systems Biology Simulation Core Library provides an efficient and exhaustive Java™ implementation of methods to interpret the content of models encoded in the Systems Biology Markup Language (SBML) and its numerical solution. This library is based on the JSBML project. It can be used on every operating system for which a Java Virtual Machine is available. Version 2.0 and beyond support simulation in three frameworks: constraint-based analysis, stochastic simulation, and ordinary differential equation systems. SBSCL supports SED-ML and COMBINE archives in OMEX format.

JARTs are Java-based Asynchronous Real Time sockets. JART uses JNA to build a pure Java TCP/IP stack upon Netmap. It is implemented in imperative style (no TCP state machine) using asynchronous programming with the help of ea-async. JART runs on both Linux (with the proper Netmap kernel module) and FreeBSD with Netmap enabled. FreeBSD 12.1+ has Netmap in the kernel by default so it works "out of the box". While JART has proven fairly robust in limited testing, it is still a work-in-progress and may not be suitable for production use. Pull requests welcome!

Meta classifier that allows standard classification algorithms to be applied to ordinal class problems. For more information see: Eibe Frank, Mark Hall: A Simple Approach to Ordinal Classification. In: 12th European Conference on Machine Learning, 145-156, 2001. Robert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, Janos A. Csirik: Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation. In: Machine Learning, Proceedings of the Nineteenth International Conference (ICML 2002), 546-553, 2002.

The Universal Java Matrix Package (UJMP) is an open source library for dense and sparse matrix computations and linear algebra in Java. In addition to the basic operations like matrix multiplication, matrix inverse or decomposition methods, it also supports visualization, JDBC import/export and many other useful functions such as mean, correlation, standard deviation, mutual information, or the replacement of missing values. It's a swiss army knife for data processing in Java, tailored to machine learning applications.

Evaluates the worth of a subset of attributes by the level of consistency in the class values when the training instances are projected onto the subset of attributes. The consistency of any subset can never be lower than that of the full set of attributes, hence the usual practice is to use this subset evaluator in conjunction with a Random or Exhaustive search which looks for the smallest subset with consistency equal to that of the full set of attributes. See: H. Liu, R. Setiono: A probabilistic approach to feature selection - A filter solution. In: 13th International Conference on Machine Learning, 319-327, 1996.

This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier. Predictions are made via majority vote, since all the generated base classifiers are put into the Vote meta classifier. Useful for base classifiers that are quadratic or worse in time behavior, regarding number of instances in the training data. For more information, see: Ting, K. M., Witten, I. H.: Stacking Bagged and Dagged Models. In: Fourteenth international Conference on Machine Learning, San Francisco, CA, 367-375, 1997.

Interactively classify through visual means. You are Presented with a scatter graph of the data against two user selectable attributes, as well as a view of the decision tree. You can create binary splits by creating polygons around data plotted on the scatter graph, as well as by allowing another classifier to take over at points in the decision tree should you see fit. For more information see: Malcolm Ware, Eibe Frank, Geoffrey Holmes, Mark Hall, Ian H. Witten (2001). Interactive machine learning: letting users build classifiers. Int. J. Hum.-Comput. Stud. 55(3):281-292.

AODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that have weaker (and hence less detrimental) independence assumptions than naive Bayes. The resulting algorithm is computationally efficient while delivering highly accurate classification on many learning tasks. For more information, see G. Webb, J. Boughton, Z. Wang (2005). Not So Naive Bayes: Aggregating One-Dependence Estimators. Machine Learning. 58(1):5-24.